Abstract: A classification engine generates, using a weighted graph, a plurality of sets of confused graphemes based on recognition data for a plurality of document images; receives an input grapheme image associated with a document image comprising a plurality of grapheme images; determines a set of recognition options for the input grapheme image, where the set of recognition options comprises a set of target characters that are similar to the input grapheme image; identifies a neural network trained to recognize a first set of confused graphemes, where the first set of confused graphemes comprises at least a portion of the set of recognition options for the input grapheme image; and determines a grapheme class for the input grapheme image using the identified neural network.

Abstract: A classification engine stores a plurality of neural networks in memory, where each neural network is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images. The classification engine receives an input grapheme image associated with a document image comprising a plurality of graphemes, determines a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image, selects a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image, and determines a grapheme class for the input grapheme image using the selected first neural network.

Abstract: A classification engine stores a plurality of neural networks in memory, where each neural network is trained to recognize a set of confused graphemes from one or more sets of confused graphemes identified in recognition data for a plurality of document images. The classification engine receives an input grapheme image associated with a document image comprising a plurality of graphemes, determines a set of recognition options for the input grapheme image, wherein the set of recognition options comprises a set of target characters that are similar to the input grapheme image, selects a first neural network from the plurality of neural networks, wherein the first neural network is trained to recognize a first set of confused graphemes, and wherein the first set of graphemes comprises at least a portion of the set of recognition options for the input grapheme image, and determines a grapheme class for the input grapheme image using the selected first neural network.